This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization.

Movable antenna (MA) is a promising technology that can enhance communication performance by properly adjusting the antenna position within a local region at transceivers. To further explore the potential of an antenna array, this article proposes a new rotatable and movable antenna (RMA) architecture where the antenna array at a base station (BS) not only employs multiple MAs but also is capable of being rotated along its yaw, pitch, and roll angles. In this context, we first characterize the wireless channel with respect to different rotation angles and antenna positions and formulate an optimization problem to maximize the downlink sum rate under practical system constraints. Subsequently, we solve the non-convex problem for single-user and multi-user scenarios, respectively. In particular, for the single-user case, we optimize the rotation angles and antenna positions to maximize the user’s rate and propose a gradient ascent (GA) algorithm based on the alternating optimization (AO) framework. For the multi-user scenario with the purpose of maximizing the sum rate of all users, we make the original problem more tractable by exploiting the Lagrangian dual transform and fractional programming (FP) techniques. On this basis, a GA-based algorithm is also proposed to jointly optimize the rotation angles and MAs’ positions together with the precoding matrix at the BS in an iterative manner. Finally, numerical results show that the RMA architecture can improve the sum rate by using the proposed algorithm to adjust rotation angles and antenna positions, compared to the element-level MA, rotatable antenna, and fixed-position antenna. Moreover, the proposed optimization algorithm outperforms its counterparts in achieving a trade-off between performance and computational complexity.

As extremely large-scale (XL) arrays advance, near-field (NF) communications have gained significant attention.With this shift, traditional far-field techniques are being revised for compatibility with new XL NF communication paradigms. This work presents NF hybrid beamforming (NF-HBF) approaches for XL-MIMO, focusing on challenges like near-field effects and spatial non-stationarity. First, it redefines the sparse recovery-based NF-HBF problem, shifting from angular- to polar-domain code-books, leading to direct greedy hybrid beamforming (DG-HBF). However, challenges such as high computational complexity, phase shifter (PS) resolution, and spatial non-stationarities persist. To overcome these, this study proposes stepwise-individual and stepwise-joint greedy HBF methods, namely SIG-HBF and SJG-HBF. These methods simplify the process by approximating spherical-wave beams with planar-wave beams, promising lower PS resolution needs, reduced complexity, and the ability to tackle spatially non-stationary channels. Moreover, by exploring conjugate symmetric sequency-ordered Hadamard transforms, NF-HBF can be efficiently achieved using 2-bit PSs with values in {1,−1,j,−j}, facilitated by the SJG-HBF and SJG-HBF methods. Numerical simulations on the proposed methods demonstrate that DG-HBF can approach NF fully-digital beamforming, while SIG-HBF and SJG-HBF highlight the feasibility of utilizing angular-domain codebooks with low PS cost and low memory storage for NF-HBF.

This paper concentrates on cell-free massive multiple-input and multiple-output (MIMO) network with variable-resolution analog-to-digital converters (ADCs). In such an architecture, all ADCs equipping at any access point (AP) can use arbitrary bit resolution to realize adaptive quantization and reduce power consumption. Under this circumstance, we first introduce a quantization-aware channel estimator based on linear minimum mean-square error (LMMSE) theory. On this basis, intra-AP and inter-AP bit allocation problems are investigated to maximize channel estimation quality subject to the total number of quantization bits. By leveraging the statistical characteristics of the estimated channels and estimation errors, we then derive the theoretical expressions of the achievable uplink spectral efficiency (SE) for maximal ratio combining (MRC) and minimum mean-square error (MMSE) combining, respectively. Furthermore, to maximize the sum SE under the constraint of total ADC quantization bits, we also investigate intra-AP and inter-AP bit allocation problems for both single-user and multi-user scenarios. Finally, simulation results confirm that our theoretical analyses are correct and accurate. In addition, we resort to numerical results to achieve some new insights and verify the advantages and conclusions pertinent to the proposed bit allocation techniques.

Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study how to exploit LBT strategies for frame based equipment (FBE) in unlicensed spectrum. We consider two optimization problems: Throughput optimal stopping and nominal throughput optimal stopping. We discover that the throughput optimal transmission strategy for FBE in unlicensed spectrum is to transmit whenever the channel is clear. In contrast, we find that the nominal throughput optimal transmission strategy is less aggressive: The FBE does not transmit until it finds that the channel is clear and the channel quality exceeds an optimized threshold.

The effort to extend cellular technologies to unlicensed spectrum has been gaining high momentum. Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study throughput optimal LBT transmission strategy for load based equipment (LBE). We find that the optimal rule is a pure threshold policy: The LBE should stop listening and transmit once the channel quality exceeds an optimized threshold. We also reveal the optimal set of LBT parameters that are compliant with regulatory requirements. Our results shed light on how the regulatory LBT requirements can affect the transmission strategies of radio equipment in unlicensed spectrum.

A performance-acceptable and low-complexity detection method for massive multiple-input multiple-output (MIMO) involving low-resolution analog digital converter (ADC) at each antenna is proposed. The proposed method combines the generalized approximate message passing (GAMP) detection with channel decoder and exchanges extrinsic information between them, by which the remaining information filtered by the ADCs can be recovered as accurate as possible. Contrasted to the iterative minimum mean squared error (MMSE) detection, our method circumvents large-scale matrix inverse operation and leverages the statistical properties of both quantization errors and transmitted symbols. Moreover, we analyze the computational complexity and storage occupation for both algorithms to authenticate the superiority of the proposed approach. For visualization, the numerical results reveal that 3-bit ADCs are capable of achieving the almost same performance as the full resolution ADCs and substantiate that the bit error ratio (BER) performance of the proposed method is equivalent to that of iterative MMSE but with less complexity for implementation.